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Joint Communications/Control Seminar: Xiaojun Li "Robust Online Algorithms for Electrical Vehicle Charging with Partial Future Knowledge”

Speaker Prof. Xiaojun Li, Purdue University
Date: 6/4/2014
Time: 3:00 pm - 4:00 pm
Location: 141 Coordinated Science Laboratory
Event Contact: Peggy Wells
Sponsor: Communications and Decision & Control Laboratory
Event Type: seminar
  TITLE: “Robust Online Algorithms for Electrical Vehicle Charging with Partial Future Knowledge” ABSTRACT: There are a number of common features for many control and decision problems in future power systems. First, there is significant uncertainty in the future operating conditions, which can arise, e.g., from the uncertainty of wind/solar generation or human consumption patterns. Second, the uncertainty is often revealed sequentially in time, and thus the decision at each instant must be based only on the information that has already been revealed. Third, the worst-case performance often has a critical impact to the overall system, e.g., if the energy supply cannot meet the demand, the entire power grid could fall apart. Thus, there is a pressing need to develop sequential decision algorithms that can achieve robust worst-case performance against future uncertainty. Competitive online algorithms could be a useful framework for solving this type of problems. In some applications, the optimal online algorithm, which achieves the smallest possible worst-case competitive ratio compared to an offline solution, can be found even when there is no prior information about the future input. In other applications, however, this optimal competitive ratio could still be quite poor. In this work, we are interested in using partial future knowledge to improve the worst-case competitive ratio of online algorithms. The challenge would be to identify the type of the partial future knowledge that is both useful and easy-to-obtain, and to develop the corresponding online algorithms that best utilize the partial future knowledge. We present one such study in the context of EV (Electric Vehicle) charging. We consider an aggregator that manages a large number of EV charging jobs, each of which requests a certain amount of energy before a deadline. The goal of the aggregator is to minimize the peak consumption at any time by intelligently scheduling the charging jobs. Here, uncertainty arises due to the unknown future arrivals of EV charging jobs. In contrast to existing approaches that either require precise knowledge of future arrivals or do not make use of any future information at all, we consider a scenario with partial future knowledge, where a fraction of the users reserve EV charging jobs (with possible reservation uncertainty) in advance. We then study how much limited future knowledge can improve the performance of online algorithms. We provide a general and systematic framework for determining the optimal competitive ratios for an arbitrary set of reservation parameters, and develop simple online algorithms that attain these optimal competitive ratios. Our numerical results indicate that reservation can significantly improve the competitive ratio and reduce the peak consumption. Bio: Xiaojun Lin received his B.S. from Zhongshan University, Guangzhou, China, in 1994, and his M.S. and Ph.D. degrees from Purdue University, West Lafayette, Indiana, in 2000 and 2005, respectively. He is currently an Associate Professor of Electrical and Computer Engineering at Purdue University. Dr. Lin's research interests are in the analysis, control and optimization of large and complex communication networks and networked systems. He received the IEEE INFOCOM 2008 best paper award and 2005 best paper of the year award from Journal of Communications and Networks. His paper was also one of two runner-up papers for the best-paper award at IEEE INFOCOM 2005. He received the NSF CAREER award in 2007. He was the Workshop co-chair for IEEE GLOBECOM 2007, the Panel co-chair for WICON 2008, the TPC co-chair for ACM MobiHoc 2009, Mini-conference co-chair for IEEE INFOCOM 2012, and Workshop chair for WiOpt 2014. He is currently serving as an Area Editor for (Elsevier) Computer Networks journal and an Associate Editor for IEEE/ACM Transactions on Networking, and has served as a Guest Editor for (Elsevier) Ad Hoc Networks journal.